Comparison of Simplified Physics-Based Building Energy Model to an Advanced Neural Network for Automatic Fault Detection

Buildings are complex structures with dynamic loading and ever-changing usage. Additionally, the need to reduce unnecessary energy consumption in buildings is increasing. As a result, buildings and building energy systems should be designed to conserve energy, and buildings should be monitored and evaluated to ensure that the designs are executed properly and that the buildings are operated correctly. Most building designers now use very adequate energy modeling software such as EnergyPlus, IES, EQUEST, and others to support the design task. However, the problem with the current lineup of programs is that they require extensive inputs for material properties and usage loads; this results in spending extensive amounts of time performing model calibration or having to adjust multiple values (sometimes hundreds) to bring a model in alignment with actual building use. As a consequence, the existing software is complex and awkward for efficient monitoring and evaluation, especially for fault detection and diagnosis. Due to the limitations of current modeling programs, development has begun on rule-based and component-based fault detection by a number of companies. However, a suitable rigorous physics-based model has not been developed for the purpose of fault detection. Consequently, this thesis research will discuss the design, development, evaluation, and testing of a model-based fault detection program and procedure as well as comparisons to state-of-the-art neural networks. Considering how complex some buildings have become, it has become important to make sure the building systems are operating as intended. Some current progress is being done by the large energy service companies in the form of logic-based fault detection for individual components. While component-based fault detection is effective, it relies on accurate sensor readings and does not account for actual building performance. This research herein is the result of the development, testing, and refinement of a simplified but rigorous and complete physics-based model for buildings and building energy systems that is purposely designed and implemented to support fault detection and similar applications. The usefulness and effectiveness of this simplified physics-based model (SPBM) is demonstrated by comparison with the obvious currently available alternative, a state of the art purely data driven neural network black-box model. The models, a simplified physics-based energy model and a neural network, will evaluated total building performance using weather and minimal load data that is common to most buildings to determine, identify, and measure the impact of building faults. Evaluation of performance and accuracy of such a system to a state-of-the-art machine learning model provides substantial insight to current and future fault detection methods.Ph.D

Designing for Mass Customization Housing through Generative Design

This research proposal aims to investigate computational design strategies for sustainable, affordable, and more equitable housing. The study will focus on the use of generative design tools, such as parametric modeling, rule-based modeling, and optimization, to aid architects and designers in creating custom housing complexes for single families in small and medium urban lots. The goal is to develop a computational method that considers sustainability, affordability, and long-term usage parameters to create housing designs that meet the desired spatial qualities. The research question asks how generative design tools can support designers in approaching affordable housing given the increasing demand for it. The study will explore a modular grid-based design approach to ensure consistency and alignment and establish connections between individual spaces to form a complete house floor plan. The proposed research will consider site constraints and analyze existing buildings, trees, and zoning regulations to tailor the aggregation system. The stochastic aggregation component will generate numerous floor plans, and the optimization component will cherry-pick specific floor plans based on the requirements to evaluate the most suitable floor plans for any given scenario

Designing for Mass Customization Housing through Generative Design

This research proposal aims to investigate computational design strategies for sustainable, affordable, and more equitable housing. The study will focus on the use of generative design tools, such as parametric modeling, rule-based modeling, and optimization, to aid architects and designers in creating custom housing complexes for single families in small and medium urban lots. The goal is to develop a computational method that considers sustainability, affordability, and long-term usage parameters to create housing designs that meet the desired spatial qualities. The research question asks how generative design tools can support designers in approaching affordable housing given the increasing demand for it. The study will explore a modular grid-based design approach to ensure consistency and alignment and establish connections between individual spaces to form a complete house floor plan. The proposed research will consider site constraints and analyze existing buildings, trees, and zoning regulations to tailor the aggregation system. The stochastic aggregation component will generate numerous floor plans, and the optimization component will cherry-pick specific floor plans based on the requirements to evaluate the most suitable floor plans for any given scenario

An Experimental Investigation of the Integration of Smart Building Components with Building Information Model (BIM)

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed

Preliminary Design of Moment-Resisting Frame Buildings for Tolerable Financial Loss

This paper aims to transfer the current performance-based seismic assessment (PBSA) methodology to engineering practice by providing a preliminary design procedure denoted herein as the preliminary performance-based seismic design (PPBSD). PPBSD aims at guiding a conceptual design given the tolerable expected loss and target hazard level. The suggested preliminary design procedure implicitly incorporates the main sources of variability in the seismic performance assessment of building structures including the variability associated with seismic excitation. PPBSD can help stakeholders make informed decisions on how to handle potential seismic risk at the preliminary design level with minimal computational effort. The process for development of design tools required for implementation of PPBSD are described and such development is illustrated for a 4-story reinforced concrete special moment-resisting frame (RC-SMRF) building located in Los Angeles for a 475-year ground motion return period. A design example is offered to demonstrate how PPBSD can be implemented in practice

Hope for the Best, Prepare for the Worst: Response of Tall Steel Buildings to the ShakeOut Scenario Earthquake

This work represents an effort to develop one plausible realization of the effects of the scenario event on tall steel moment-frame buildings. We have used the simulated ground motions with three-dimensional nonlinear finite element models of three buildings in the 20-story class to simulate structural responses at 784 analysis sites spaced at approximately 4 km throughout the San Fernando Valley, the San Gabriel Valley, and the Los Angeles Basin. Based on the simulation results and available information on the number and distribution of steel buildings, the recommended damage scenario for the ShakeOut drill was 5% of the estimated 150 steel moment-frame structures in the 10–30 story range collapsing, 10% red-tagged, 15% with damage serious enough to cause loss of life, and 20% with visible damage requiring building closure

An Assessment to Benchmark the Seismic Performance of a Code-Conforming Reinforced-Concrete Moment-Frame Building

This report describes a state-of-the-art performance-based earthquake engineering methodology that is used to assess the seismic performance of a four-story reinforced concrete (RC) office building that is generally representative of low-rise office buildings constructed in highly seismic regions of California. This “benchmark” building is considered to be located at a site in the Los Angeles basin, and it was designed with a ductile RC special moment-resisting frame as its seismic lateral system that was designed according to modern building codes and standards. The building’s performance is quantified in terms of structural behavior up to collapse, structural and nonstructural damage and associated repair costs, and the risk of fatalities and their associated economic costs. To account for different building configurations that may be designed in practice to meet requirements of building size and use, eight structural design alternatives are used in the performance assessments. Our performance assessments account for important sources of uncertainty in the ground motion hazard, the structural response, structural and nonstructural damage, repair costs, and life-safety risk. The ground motion hazard characterization employs a site-specific probabilistic seismic hazard analysis and the evaluation of controlling seismic sources (through disaggregation) at seven ground motion levels (encompassing return periods ranging from 7 to 2475 years). Innovative procedures for ground motion selection and scaling are used to develop acceleration time history suites corresponding to each of the seven ground motion levels. Structural modeling utilizes both “fiber” models and “plastic hinge” models. Structural modeling uncertainties are investigated through comparison of these two modeling approaches, and through variations in structural component modeling parameters (stiffness, deformation capacity, degradation, etc.). Structural and nonstructural damage (fragility) models are based on a combination of test data, observations from post-earthquake reconnaissance, and expert opinion. Structural damage and repair costs are modeled for the RC beams, columns, and slabcolumn connections. Damage and associated repair costs are considered for some nonstructural building components, including wallboard partitions, interior paint, exterior glazing, ceilings, sprinkler systems, and elevators. The risk of casualties and the associated economic costs are evaluated based on the risk of structural collapse, combined with recent models on earthquake fatalities in collapsed buildings and accepted economic modeling guidelines for the value of human life in loss and cost-benefit studies. The principal results of this work pertain to the building collapse risk, damage and repair cost, and life-safety risk. These are discussed successively as follows. When accounting for uncertainties in structural modeling and record-to-record variability (i.e., conditional on a specified ground shaking intensity), the structural collapse probabilities of the various designs range from 2% to 7% for earthquake ground motions that have a 2% probability of exceedance in 50 years (2475 years return period). When integrated with the ground motion hazard for the southern California site, the collapse probabilities result in mean annual frequencies of collapse in the range of [0.4 to 1.4]x10 -4 for the various benchmark building designs. In the development of these results, we made the following observations that are expected to be broadly applicable: (1) The ground motions selected for performance simulations must consider spectral shape (e.g., through use of the epsilon parameter) and should appropriately account for correlations between motions in both horizontal directions; (2) Lower-bound component models, which are commonly used in performance-based assessment procedures such as FEMA 356, can significantly bias collapse analysis results; it is more appropriate to use median component behavior, including all aspects of the component model (strength, stiffness, deformation capacity, cyclic deterioration, etc.); (3) Structural modeling uncertainties related to component deformation capacity and post-peak degrading stiffness can impact the variability of calculated collapse probabilities and mean annual rates to a similar degree as record-to-record variability of ground motions. Therefore, including the effects of such structural modeling uncertainties significantly increases the mean annual collapse rates. We found this increase to be roughly four to eight times relative to rates evaluated for the median structural model; (4) Nonlinear response analyses revealed at least six distinct collapse mechanisms, the most common of which was a story mechanism in the third story (differing from the multi-story mechanism predicted by nonlinear static pushover analysis); (5) Soil-foundation-structure interaction effects did not significantly affect the structural response, which was expected given the relatively flexible superstructure and stiff soils. The potential for financial loss is considerable. Overall, the calculated expected annual losses (EAL) are in the range of $52,000 to$97,000 for the various code-conforming benchmark building designs, or roughly 1% of the replacement cost of the building ($8.8M). These losses are dominated by the expected repair costs of the wallboard partitions (including interior paint) and by the structural members. Loss estimates are sensitive to details of the structural models, especially the initial stiffness of the structural elements. Losses are also found to be sensitive to structural modeling choices, such as ignoring the tensile strength of the concrete (40 EAL) or the contribution of the gravity frames to overall building stiffness and strength (15 change in EAL). Although there are a number of factors identified in the literature as likely to affect the risk of human injury during seismic events, the casualty modeling in this study focuses on those factors (building collapse, building occupancy, and spatial location of building occupants) that directly inform the building design process. The expected annual number of fatalities is calculated for the benchmark building, assuming that an earthquake can occur at any time of any day with equal probability and using fatality probabilities conditioned on structural collapse and based on empirical data. The expected annual number of fatalities for the code-conforming buildings ranges between 0.05*10 -2 and 0.21*10 -2 , and is equal to 2.30*10 -2 for a non-code conforming design. The expected loss of life during a seismic event is perhaps the decision variable that owners and policy makers will be most interested in mitigating. The fatality estimation carried out for the benchmark building provides a methodology for comparing this important value for various building designs, and enables informed decision making during the design process. The expected annual loss associated with fatalities caused by building earthquake damage is estimated by converting the expected annual number of fatalities into economic terms. Assuming the value of a human life is$3.5M, the fatality rate translates to an EAL due to fatalities of $3,500 to$5,600 for the code-conforming designs, and $79,800 for the non-code conforming design. Compared to the EAL due to repair costs of the code-conforming designs, which are on the order of$66,000, the monetary value associated with life loss is small, suggesting that the governing factor in this respect will be the maximum permissible life-safety risk deemed by the public (or its representative government) to be appropriate for buildings. Although the focus of this report is on one specific building, it can be used as a reference for other types of structures. This report is organized in such a way that the individual core chapters (4, 5, and 6) can be read independently. Chapter 1 provides background on the performance-based earthquake engineering (PBEE) approach. Chapter 2 presents the implementation of the PBEE methodology of the PEER framework, as applied to the benchmark building. Chapter 3 sets the stage for the choices of location and basic structural design. The subsequent core chapters focus on the hazard analysis (Chapter 4), the structural analysis (Chapter 5), and the damage and loss analyses (Chapter 6). Although the report is self-contained, readers interested in additional details can find them in the appendices